Yaru Hao

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 8 papers; trusts 402 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 272 citations into it; #16,582 of 35,345 by trust here, #3,582 by citations
• Computer Vision and Pattern Recognition — 176 citations into it
• Natural Language Processing — 170 citations into it; #5,089 of 5,688 by trust here, #4,983 by citations
• Large Language Models — 89 citations into it; #361 of 799 by trust here, #397 by citations
• Evaluation & Benchmarks — 86 citations into it
• Multimodal Learning — 80 citations into it
• Model Architectures — 37 citations into it
• Representation & Self-Supervised Learning — 26 citations into it

Whom Yaru Hao trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Alex Krizhevsky
6. Kaiming He
7. Alec Radford
8. Yann LeCun
9. Sepp Hochreiter
10. Jimmy Ba

Who trusts Yaru Hao overall

1. Li Dong
2. Hongyu Wang
3. Marius Mosbach
4. Kaleel Mahmood
5. Damai Dai
6. Hangbo Bao
7. Ninareh Mehrabi
8. Kevin Meng
9. Shuming Ma
10. Shaohan Huang

Papers from Yaru Hao's perspective

Ordered by how strongly the researchers Yaru Hao trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Language Is Not All You Need: Aligning Perception with Language Models (2023) — 786 citations
• Kosmos-2: Grounding Multimodal Large Language Models to the World (2023) — 1,249 citations
• Language Models are General-Purpose Interfaces (2022) — 113 citations
• Visualizing and Understanding the Effectiveness of BERT (2019) — 205 citations
• Knowledge Neurons in Pretrained Transformers (2021) — 725 citations
• Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers (2023) — 401 citations
• Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT (2021)
• Self-Attention Attribution: Interpreting Information Interactions Inside Transformer (2020) — 300 citations